{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "suffering-fitness",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from sklearn import preprocessing\n",
    "flag=0\n",
    "# 读取体素模型的文件\n",
    "def read(path):\n",
    "    result = []\n",
    "    with open(path, 'r') as f:\n",
    "        for line in f.readlines():\n",
    "            temp = []\n",
    "            for a in line.strip().split(','):\n",
    "                temp.append(float(a))\n",
    "            result.append(temp)\n",
    "    # result是[[x,y,z,0],[x1,y1,z1,1],....]的形式，你可以直接读取\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "rental-samoa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer_sizes():\n",
    "    n_x = 3  #x,y,z\n",
    "    n_h = 16 #定义隐藏层size\n",
    "    n_y = 4  #a,b,c,d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "interstate-webster",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    W1 =np.random.rand(n_h,n_x)*0.01\n",
    "    b1 = np.random.rand(n_h,1)*0.01\n",
    "    #print(W1)\n",
    "    #print(b1)\n",
    "    parameters={\"W1\":W1,\"b1\":b1}\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "driving-internet",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(X, parameters):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    xyz=X[:,:3]\n",
    "    Z1 = np.dot(xyz,W1.T) + b1.T  #Z1=AX+BY+CZ+D 返回(3万,3)*(3,16)+(1,16)=(3万，16)\n",
    "    return Z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fourth-basement",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(Z1, X, parameters):\n",
    "    '''\n",
    "        第一列表示表面点为正或0的个数，\n",
    "        第二列表示表面点为负的个数,\n",
    "        第三列表示外点为正或0的个数，\n",
    "        第四列表示外点为负的个数。\n",
    "        首先选出表面点不管正负最多的几行，\n",
    "        然后选取外点符号相反数目最多的那个\n",
    "    '''\n",
    "    cnt=np.zeros((16,4)) \n",
    "    for i in range(len(Z1)):\n",
    "        if(X[i][3]>0):\n",
    "            for j in range(len(Z1[i])):\n",
    "                if(Z1[i][j]>=0):\n",
    "                    cnt[j][0]+=1\n",
    "                if(Z1[i][j]<0):\n",
    "                    cnt[j][1]+=1\n",
    "        if(X[i][3]==0):\n",
    "            for j in range(len(Z1[i])):\n",
    "                if(Z1[i][j]>=0):\n",
    "                    cnt[j][2]+=1\n",
    "                if(Z1[i][j]<0):\n",
    "                    cnt[j][3]+=1\n",
    "    #最后要返回一个a,b,c,d的数组\n",
    "    max1=0\n",
    "    max2=0\n",
    "    ret=0\n",
    "    #print(cnt)\n",
    "    for i in range(len(cnt)):\n",
    "        if(cnt[i][0]>=max1):\n",
    "            flag=0\n",
    "            if(cnt[i][0]==max1 and cnt[i][3]>max2):\n",
    "                max1=cnt[i][0]\n",
    "                max2=cnt[i][3]\n",
    "                ret=i\n",
    "            if(cnt[i][0]>max1):\n",
    "                max1=cnt[i][0]\n",
    "                max2=cnt[i][3]\n",
    "                ret=i\n",
    "        if(cnt[i][1]>=max1):\n",
    "            flag=1\n",
    "            if(cnt[i][1]==max1 and cnt[i][2]>max2):\n",
    "                max1=cnt[i][1]\n",
    "                max2=cnt[i][2]\n",
    "                ret=i\n",
    "            if(cnt[i][1]>max1):\n",
    "                max1=cnt[i][1]\n",
    "                max2=cnt[i][2]\n",
    "                ret=i\n",
    "    a,b,c=parameters[\"W1\"][ret]\n",
    "    d=parameters[\"b1\"][ret]\n",
    "    cache=[a,b,c,d]\n",
    "    print(\"内点数\",max1,\"外点数\",max2)\n",
    "    print(cache)\n",
    "    return cache,max1,max2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adjustable-costume",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fantastic-thailand",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation(cache, X, rate, i):\n",
    "    rate=rate*0.625**(i//5)\n",
    "    li=[]\n",
    "    cache=np.array(cache)\n",
    "    for a in [1,-1]:\n",
    "        for b in [1,-1]:\n",
    "            for c in [1,-1]:\n",
    "                for d in [1,-1]:\n",
    "                    temp=[a,b,c,d]\n",
    "                    li.append(temp)\n",
    "    li=np.array(li)*rate\n",
    "    W1=li[:,:3]+cache[:3]\n",
    "    b1=li[:,3]+cache[3]\n",
    "    parameters={\"W1\":W1,\"b1\":b1}\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "downtown-start",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_model(X, num_iterations = 1000, print_cost=False):\n",
    "    n_x = 3  #x,y,z\n",
    "    n_h = 16 #定义隐藏层size\n",
    "    n_y = 4  #a,b,c,d\n",
    "    rate=0.1\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    for i in range(num_iterations):\n",
    "        print(\"iteration\",i+1)\n",
    "        Z1 = forward_propagation(X, parameters)\n",
    "        #print(Z1)\n",
    "        cache,max1,max2 = compute_cost(Z1, X, parameters)\n",
    "        parameters = backward_propagation(cache, X, rate,i+1)\n",
    "    return cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mechanical-importance",
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_face(X,cache):\n",
    "    a,b,c,d=cache\n",
    "    W=np.array([[a,b,c,d]])\n",
    "    num=0\n",
    "    Y=np.dot(X[:,:3],W[:,:3].T)+W[:,3].T\n",
    "    if(flag==0):\n",
    "        for i in range(len(X)):\n",
    "            if(X[i][3]==0 and Y[i]<0):\n",
    "                X[i][3]=-0.2\n",
    "                num+=1\n",
    "    if(flag==1):\n",
    "         for i in range(len(X)):\n",
    "            if(X[i][3]==0 and Y[i]>=0):\n",
    "                X[i][3]=-0.2\n",
    "                num+=1\n",
    "    print(\"num:\",num)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "funny-mouth",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "face: 1\n",
      "iteration 1\n",
      "内点数 584.0 外点数 0.0\n",
      "[0.0050547491526987745, 0.009756893549043712, 0.0044267197318946395, array([0.0091997])]\n",
      "iteration 2\n",
      "内点数 584.0 外点数 566.0\n",
      "[0.10505474915269877, 0.10975689354904372, 0.10442671973189464, -0.09080030107380312]\n",
      "iteration 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-09e1bf7c416b>:4: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  cache=np.array(cache)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "内点数 584.0 外点数 704.0\n",
      "[0.20505474915269878, 0.20975689354904373, 0.20442671973189463, -0.19080030107380314]\n",
      "iteration 4\n",
      "内点数 584.0 外点数 743.0\n",
      "[0.3050547491526988, 0.30975689354904373, 0.3044267197318946, -0.29080030107380317]\n",
      "iteration 5\n",
      "内点数 584.0 外点数 785.0\n",
      "[0.4050547491526988, 0.40975689354904377, 0.4044267197318946, -0.39080030107380315]\n",
      "iteration 6\n",
      "内点数 584.0 外点数 1053.0\n",
      "[0.4675547491526988, 0.34725689354904377, 0.4669267197318946, -0.45330030107380315]\n",
      "iteration 7\n",
      "内点数 584.0 外点数 1084.0\n",
      "[0.4050547491526988, 0.28475689354904377, 0.4044267197318946, -0.39080030107380315]\n",
      "iteration 8\n",
      "内点数 584.0 外点数 1053.0\n",
      "[0.4675547491526988, 0.34725689354904377, 0.4669267197318946, -0.45330030107380315]\n",
      "iteration 9\n",
      "内点数 584.0 外点数 1084.0\n",
      "[0.4050547491526988, 0.28475689354904377, 0.4044267197318946, -0.39080030107380315]\n",
      "iteration 10\n",
      "内点数 584.0 外点数 1053.0\n",
      "[0.4675547491526988, 0.34725689354904377, 0.4669267197318946, -0.45330030107380315]\n",
      "iteration 11\n",
      "内点数 584.0 外点数 1066.0\n",
      "[0.4284922491526988, 0.30819439354904377, 0.4278642197318946, -0.41423780107380315]\n",
      "iteration 12\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3894297491526988, 0.26913189354904377, 0.3888017197318946, -0.37517530107380315]\n",
      "iteration 13\n",
      "内点数 584.0 外点数 1066.0\n",
      "[0.4284922491526988, 0.30819439354904377, 0.4278642197318946, -0.41423780107380315]\n",
      "iteration 14\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3894297491526988, 0.26913189354904377, 0.3888017197318946, -0.37517530107380315]\n",
      "iteration 15\n",
      "内点数 584.0 外点数 1066.0\n",
      "[0.4284922491526988, 0.30819439354904377, 0.4278642197318946, -0.41423780107380315]\n",
      "iteration 16\n",
      "内点数 584.0 外点数 1085.0\n",
      "[0.4040781866526988, 0.28378033104904377, 0.4034501572318946, -0.38982373857380315]\n",
      "iteration 17\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3796641241526988, 0.25936626854904377, 0.3790360947318946, -0.36540967607380315]\n",
      "iteration 18\n",
      "内点数 584.0 外点数 1085.0\n",
      "[0.4040781866526988, 0.28378033104904377, 0.4034501572318946, -0.38982373857380315]\n",
      "iteration 19\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3796641241526988, 0.25936626854904377, 0.3790360947318946, -0.36540967607380315]\n",
      "iteration 20\n",
      "内点数 584.0 外点数 1085.0\n",
      "[0.4040781866526988, 0.28378033104904377, 0.4034501572318946, -0.38982373857380315]\n",
      "iteration 21\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3888193975901988, 0.26852154198654377, 0.3881913681693946, -0.37456494951130315]\n",
      "iteration 22\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3735606085276988, 0.25326275292404377, 0.3729325791068946, -0.35930616044880315]\n",
      "iteration 23\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3888193975901988, 0.26852154198654377, 0.3881913681693946, -0.37456494951130315]\n",
      "iteration 24\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3735606085276988, 0.25326275292404377, 0.3729325791068946, -0.35930616044880315]\n",
      "iteration 25\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3888193975901988, 0.26852154198654377, 0.3881913681693946, -0.37456494951130315]\n",
      "iteration 26\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3792826544261363, 0.25898479882248127, 0.3786546250053321, -0.36502820634724065]\n",
      "iteration 27\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3888193975901988, 0.26852154198654377, 0.3881913681693946, -0.37456494951130315]\n",
      "iteration 28\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3792826544261363, 0.25898479882248127, 0.3786546250053321, -0.36502820634724065]\n",
      "iteration 29\n",
      "内点数 584.0 外点数 1091.0\n",
      "[0.3888193975901988, 0.26852154198654377, 0.3881913681693946, -0.37456494951130315]\n",
      "iteration 30\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3792826544261363, 0.25898479882248127, 0.3786546250053321, -0.36502820634724065]\n",
      "iteration 31\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3733221899485972, 0.2530243343449422, 0.37269416052779303, -0.3590677418697016]\n",
      "iteration 32\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3792826544261363, 0.25898479882248127, 0.3786546250053321, -0.36502820634724065]\n",
      "iteration 33\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3733221899485972, 0.2530243343449422, 0.37269416052779303, -0.3590677418697016]\n",
      "iteration 34\n",
      "内点数 584.0 外点数 1097.0\n",
      "[0.3792826544261363, 0.25898479882248127, 0.3786546250053321, -0.36502820634724065]\n",
      "iteration 35\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3733221899485972, 0.2530243343449422, 0.37269416052779303, -0.3590677418697016]\n",
      "iteration 36\n",
      "内点数 584.0 外点数 1100.0\n",
      "[0.37704748024705914, 0.2567496246434041, 0.37641945082625494, -0.3627930321681635]\n",
      "iteration 37\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3733221899485972, 0.2530243343449422, 0.37269416052779303, -0.3590677418697016]\n",
      "iteration 38\n",
      "内点数 584.0 外点数 1100.0\n",
      "[0.37704748024705914, 0.2567496246434041, 0.37641945082625494, -0.3627930321681635]\n",
      "iteration 39\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.3733221899485972, 0.2530243343449422, 0.37269416052779303, -0.3590677418697016]\n",
      "iteration 40\n",
      "内点数 584.0 外点数 1100.0\n",
      "[0.37704748024705914, 0.2567496246434041, 0.37641945082625494, -0.3627930321681635]\n",
      "iteration 41\n",
      "内点数 584.0 外点数 1101.0\n",
      "[0.37471917381052045, 0.2544213182068654, 0.37409114438971625, -0.3604647257316248]\n",
      "iteration 42\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37239086737398175, 0.2520930117703267, 0.37176283795317755, -0.3581364192950861]\n",
      "iteration 43\n",
      "内点数 584.0 外点数 1101.0\n",
      "[0.37471917381052045, 0.2544213182068654, 0.37409114438971625, -0.3604647257316248]\n",
      "iteration 44\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37239086737398175, 0.2520930117703267, 0.37176283795317755, -0.3581364192950861]\n",
      "iteration 45\n",
      "内点数 584.0 外点数 1101.0\n",
      "[0.37471917381052045, 0.2544213182068654, 0.37409114438971625, -0.3604647257316248]\n",
      "iteration 46\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37326398228768376, 0.25296612668402874, 0.37263595286687956, -0.3590095342087881]\n",
      "iteration 47\n",
      "内点数 584.0 外点数 1103.0\n",
      "[0.3718087907648471, 0.25151093516119205, 0.3711807613440429, -0.35755434268595143]\n",
      "iteration 48\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37326398228768376, 0.25296612668402874, 0.37263595286687956, -0.3590095342087881]\n",
      "iteration 49\n",
      "内点数 584.0 外点数 1103.0\n",
      "[0.3718087907648471, 0.25151093516119205, 0.3711807613440429, -0.35755434268595143]\n",
      "iteration 50\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37326398228768376, 0.25296612668402874, 0.37263595286687956, -0.3590095342087881]\n",
      "iteration 51\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37235448758591083, 0.2520566319822558, 0.37172645816510663, -0.3581000395070152]\n",
      "iteration 52\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.3714449928841379, 0.2511471372804829, 0.3708169634633337, -0.35719054480524226]\n",
      "iteration 53\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37235448758591083, 0.2520566319822558, 0.37172645816510663, -0.3581000395070152]\n",
      "iteration 54\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.3714449928841379, 0.2511471372804829, 0.3708169634633337, -0.35719054480524226]\n",
      "iteration 55\n",
      "内点数 584.0 外点数 1102.0\n",
      "[0.37235448758591083, 0.2520566319822558, 0.37172645816510663, -0.3581000395070152]\n",
      "iteration 56\n",
      "内点数 584.0 外点数 1103.0\n",
      "[0.37178605339730275, 0.2514881977936477, 0.37115802397649855, -0.3575316053184071]\n",
      "iteration 57\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.37121761920869467, 0.25091976360503965, 0.37058958978789047, -0.35696317112979903]\n",
      "iteration 58\n",
      "内点数 584.0 外点数 1103.0\n",
      "[0.37178605339730275, 0.2514881977936477, 0.37115802397649855, -0.3575316053184071]\n",
      "iteration 59\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.37121761920869467, 0.25091976360503965, 0.37058958978789047, -0.35696317112979903]\n",
      "iteration 60\n",
      "内点数 584.0 外点数 1103.0\n",
      "[0.37178605339730275, 0.2514881977936477, 0.37115802397649855, -0.3575316053184071]\n",
      "iteration 61\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.3714307820294227, 0.2511329264257677, 0.3708027526086185, -0.35717633395052706]\n",
      "iteration 62\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.37107551066154265, 0.2507776550578876, 0.37044748124073845, -0.356821062582647]\n",
      "iteration 63\n",
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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    },
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[0.40093882254770974, 0.28032620879465797, 0.40749801074036096, -0.39218499120756667]\n",
      "iteration 80\n",
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      "[0.4010255587215086, 0.2804129449684568, 0.4074112745665621, -0.3922717273813655]\n",
      "iteration 81\n",
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      "[0.40107976883013285, 0.2804671550770811, 0.4074654846751864, -0.3923259374899898]\n",
      "iteration 82\n",
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      "[0.40113397893875713, 0.28052136518570536, 0.40751969478381067, -0.39238014759861406]\n",
      "iteration 83\n",
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      "[0.4011881890473814, 0.28057557529432964, 0.40757390489243495, -0.39243435770723833]\n",
      "iteration 84\n",
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      "[0.4012423991560057, 0.2806297854029539, 0.40751969478381067, -0.3924885678158626]\n",
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      "[0.40129660926462996, 0.2806839955115782, 0.40757390489243495, -0.3925427779244869]\n",
      "iteration 86\n",
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      "[0.4013304905825201, 0.28071787682946836, 0.4076077862103251, -0.39257665924237706]\n",
      "iteration 87\n",
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      "[0.4013643719004103, 0.28075175814735853, 0.40757390489243495, -0.39261054056026723]\n",
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      "[0.40139825321830047, 0.2807856394652487, 0.4076077862103251, -0.3926444218781574]\n",
      "iteration 89\n",
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      "[0.40143213453619064, 0.2808195207831389, 0.40757390489243495, -0.3926783031960476]\n",
      "iteration 90\n",
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      "[0.4014660158540808, 0.28085340210102905, 0.4076077862103251, -0.39271218451393775]\n",
      "iteration 91\n",
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      "[0.40148719167776215, 0.2808745779247104, 0.40762896203400645, -0.3927333603376191]\n",
      "iteration 92\n",
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      "[0.4015083675014435, 0.2808957537483917, 0.4076501378576878, -0.3927545361613004]\n",
      "iteration 93\n",
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      "[0.4015295433251248, 0.28091692957207304, 0.40762896203400645, -0.39277571198498173]\n",
      "iteration 94\n",
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      "[0.40155071914880613, 0.28093810539575437, 0.4076501378576878, -0.39279688780866306]\n",
      "iteration 95\n",
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      "[0.40157189497248746, 0.2809592812194357, 0.40762896203400645, -0.3928180636323444]\n",
      "iteration 96\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.40158512986228834, 0.28097251610923657, 0.4076421969238073, -0.39283129852214527]\n",
      "iteration 97\n",
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      "[0.4015983647520892, 0.28098575099903744, 0.4076554318136082, -0.39284453341194614]\n",
      "iteration 98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "内点数 584.0 外点数 1104.0\n",
      "[0.4016115996418901, 0.2809989858888383, 0.40766866670340907, -0.392857768301747]\n",
      "iteration 99\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.40162483453169096, 0.2810122207786392, 0.40768190159320994, -0.3928710031915479]\n",
      "iteration 100\n",
      "内点数 584.0 外点数 1104.0\n",
      "[0.40163806942149183, 0.28102545566844006, 0.40766866670340907, -0.39288423808134876]\n"
     ]
    }
   ],
   "source": [
    "li=read('D:/codeFile/Python/facePointTargetXin.txt')\n",
    "X=np.array(li)\n",
    "\n",
    "#归一化操作\n",
    "#min_max_scaler = preprocessing.MinMaxScaler()\n",
    "#X = min_max_scaler.fit_transform(X)\n",
    "#print(X)\n",
    "\n",
    "face=30\n",
    "epoch=100\n",
    "ans=[]\n",
    "for i in range(face):\n",
    "    print(\"face:\",i+1)\n",
    "    cache=nn_model(X,epoch,True)\n",
    "    #X=move_face(X,cache)\n",
    "    ans.append(cache)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "specified-absence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.37162722675758286, 0.25129054146955976, 0.3706740705162387, -0.35733394899431914], [-0.07582446818505206, -0.07529953492842406, -0.07258569270894588, 0.0835851595181088], [-0.09633203586509934, -0.09593300688507267, -0.09120350202015054, 0.10580737972120569], [0.4003082308838902, 0.27799254163321574, 0.3986878671730376, -0.388281416633402], [-0.09039069944791392, -0.09076414479651007, -0.09137612701548392, 0.1022050362142928], [0.38224151724184385, 0.264330530702768, 0.3833351007597383, -0.3710878168849694], [-0.06009586577546572, -0.0625937780967823, -0.06296792541761573, 0.06937280201207649], [0.20599715195970839, 0.20602886921350322, 0.20270354780727673, -0.39400217984827146], [0.40731959974595133, 0.2792574669414065, 0.406685777462756, -0.39369742257075757], [-0.05827699989542495, -0.060656534288658526, -0.06091885372578876, 0.06722821889983083], [-0.09514379860385092, -0.08712131807752987, -0.09516686362618537, 0.1031461275323339], [-0.0485825710449265, -0.048325459989056685, -0.04883406229461831, 0.054666764912460974], [-0.062340262605280417, -0.06227285682442682, -0.06287614553586543, 0.07031162700663737], [0.3760251419120637, 0.2561156931566343, 0.37727597393244505, -0.36316968294047225], [0.40351603008579406, 0.27550053239124184, 0.39765597053790047, -0.3875428694379346], [-0.08224960897615541, -0.08271975813886398, -0.08332540985937892, 0.09309164291986435], [0.40467042221668525, 0.2752821912450618, 0.4068253789294529, -0.3907843381078857], [-0.09106424792862264, 0.09551463420491174, 0.09551892229167677, -0.0886840171301809], [0.3990636479718942, 0.2795345852093477, 0.3983165234569572, -0.3886868008360017], [0.4167030244666289, 0.29730544435065276, 0.4190940953721578, -0.4095693269774987], [0.38094222017133517, 0.25832639431469673, 0.38222730424712714, -0.36735144783495066], [0.402624583365885, 0.2738359889963389, 0.39897416427264487, -0.38696947879114485], [-0.06561626521998218, -0.06539915686138387, -0.06077950477383337, 0.07140149827296349], [0.4215437938146318, 0.29582580646209, 0.42087417618160416, -0.41095476570116785], [-0.08178706430855517, -0.08223364940229771, -0.08267588785552965, 0.09252585755074719], [-0.09136772888788039, -0.08607152774870122, -0.09083176249763264, 0.10007691685047693], [0.398603189617712, 0.27585999738304257, 0.398498039040334, -0.3868335409431436], [-0.08012367920582453, -0.08409743178588602, -0.082940888047389, 0.09222180754715628], [-0.07176167237354303, -0.06565379164344312, -0.07147190492633854, 0.07766130296081453], [0.40163806942149183, 0.28102545566844006, 0.40766866670340907, -0.39288423808134876]]\n"
     ]
    }
   ],
   "source": [
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "brave-chemistry",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "grave-income",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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